Class SelectFdrScikitsLearnNode

Filter: Select the p-values for an estimated false discovery rate
This node has been automatically generated by wrapping the ``sklearn.feature_selection.univariate_selection.SelectFdr`` class
from the ``sklearn`` library. The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
This uses the Benjamini-Hochberg procedure. ``alpha`` is an upper bound
on the expected false discovery rate.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
**Parameters**
score_func : callable
Function taking two arrays X and y, and returning a pair of arrays
(scores, pvalues).
alpha : float, optional
The highest uncorrected p-value for features to keep.
**Attributes**
``scores_`` : array-like, shape=(n_features,)
Scores of features.
``pvalues_`` : array-like, shape=(n_features,)
p-values of feature scores.
**References**
http://en.wikipedia.org/wiki/False_discovery_rate
See also
f_classif: ANOVA F-value between labe/feature for classification tasks.
chi2: Chi-squared stats of non-negative features for classification tasks.
f_regression: F-value between label/feature for regression tasks.
SelectPercentile: Select features based on percentile of the highest scores.
SelectKBest: Select features based on the k highest scores.
SelectFpr: Select features based on a false positive rate test.
SelectFwe: Select features based on family-wise error rate.
GenericUnivariateSelect: Univariate feature selector with configurable mode.

Filter: Select the p-values for an estimated false discovery rate
This node has been automatically generated by wrapping the ``sklearn.feature_selection.univariate_selection.SelectFdr`` class
from the ``sklearn`` library. The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
This uses the Benjamini-Hochberg procedure. ``alpha`` is an upper bound
on the expected false discovery rate.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
**Parameters**
score_func : callable
Function taking two arrays X and y, and returning a pair of arrays
(scores, pvalues).
alpha : float, optional
The highest uncorrected p-value for features to keep.
**Attributes**
``scores_`` : array-like, shape=(n_features,)
Scores of features.
``pvalues_`` : array-like, shape=(n_features,)
p-values of feature scores.
**References**
http://en.wikipedia.org/wiki/False_discovery_rate
See also
f_classif: ANOVA F-value between labe/feature for classification tasks.
chi2: Chi-squared stats of non-negative features for classification tasks.
f_regression: F-value between label/feature for regression tasks.
SelectPercentile: Select features based on percentile of the highest scores.
SelectKBest: Select features based on the k highest scores.
SelectFpr: Select features based on a false positive rate test.
SelectFwe: Select features based on family-wise error rate.
GenericUnivariateSelect: Univariate feature selector with configurable mode.

_stop_training(self,
**kwargs)

execute(self,
x)

Reduce X to the selected features.

This node has been automatically generated by wrapping the sklearn.feature_selection.univariate_selection.SelectFdr class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.

is_trainable()Static Method

stop_training(self,
**kwargs)

Run score function on (X, y) and get the appropriate features.

This node has been automatically generated by wrapping the sklearn.feature_selection.univariate_selection.SelectFdr class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.